
"""simple docstring"""






import argparse
import collections

import torch
from flax import traverse_util
from tax import checkpoints

from transformers import TaConfig, TaEncoderModel, TaForConditionalGeneration
from transformers.utils import logging


logging.set_verbosity_info()




def __magic_name__	(				_lowerCamelCase:		int,					_lowerCamelCase:		Optional[int],					_lowerCamelCase:		Dict,					_lowerCamelCase:		str="attention"			)					->						str:

	'''simple docstring'''




	lowerCAmelCase												=  params[F"""{prefix}/layers_{i}/{layer_name}/key/kernel"""]
	lowerCAmelCase												=  params[F"""{prefix}/layers_{i}/{layer_name}/out/kernel"""]
	lowerCAmelCase												=  params[F"""{prefix}/layers_{i}/{layer_name}/query/kernel"""]
	lowerCAmelCase												=  params[F"""{prefix}/layers_{i}/{layer_name}/value/kernel"""]
	return k, o, q, v




def __magic_name__	(				_lowerCamelCase:		Dict,					_lowerCamelCase:		Any,					_lowerCamelCase:		str,					_lowerCamelCase:		Optional[Any]=False			)					->						Optional[int]:

	'''simple docstring'''




	if split_mlp_wi:
		lowerCAmelCase												=  params[F"""{prefix}/layers_{i}/mlp/wi_0/kernel"""]
		lowerCAmelCase												=  params[F"""{prefix}/layers_{i}/mlp/wi_1/kernel"""]
		lowerCAmelCase												=  (wi_a, wi_a)
	else:
		lowerCAmelCase												=  params[F"""{prefix}/layers_{i}/mlp/wi/kernel"""]

	lowerCAmelCase												=  params[F"""{prefix}/layers_{i}/mlp/wo/kernel"""]
	return wi, wo




def __magic_name__	(				_lowerCamelCase:		Any,					_lowerCamelCase:		Tuple,					_lowerCamelCase:		Dict,					_lowerCamelCase:		Optional[Any]			)					->						str:

	'''simple docstring'''




	return params[F"""{prefix}/layers_{i}/{layer_name}/scale"""]




def __magic_name__	(				_lowerCamelCase:		dict,					*, _lowerCamelCase:		int,					_lowerCamelCase:		bool			)					->						Any:

	'''simple docstring'''




	lowerCAmelCase												=  traverse_util.flatten_dict(variables['''target''']			)
	lowerCAmelCase												=  {'''/'''.join(_lowerCamelCase			): v for k, v in old.items()}

	# v1.1 models have a gated GeLU with wi_0 and wi_1 instead of wi
	lowerCAmelCase												=  '''encoder/layers_0/mlp/wi_0/kernel''' in old
	print('''Split MLP:''',					_lowerCamelCase			)

	lowerCAmelCase												=  collections.OrderedDict()

	# Shared embeddings.
	lowerCAmelCase												=  old['''token_embedder/embedding''']

	# Encoder.
	for i in range(_lowerCamelCase			):
		# Block i, layer 0 (Self Attention).
		lowerCAmelCase												=  tax_layer_norm_lookup(_lowerCamelCase,					_lowerCamelCase,					'''encoder''',					'''pre_attention_layer_norm'''			)
		lowerCAmelCase							,       lowerCAmelCase							,       lowerCAmelCase							,       lowerCAmelCase												=  tax_attention_lookup(_lowerCamelCase,					_lowerCamelCase,					'''encoder''',					'''attention'''			)
		lowerCAmelCase												=  layer_norm
		lowerCAmelCase												=  k.T
		lowerCAmelCase												=  o.T
		lowerCAmelCase												=  q.T
		lowerCAmelCase												=  v.T

		# Block i, layer 1 (MLP).
		lowerCAmelCase												=  tax_layer_norm_lookup(_lowerCamelCase,					_lowerCamelCase,					'''encoder''',					'''pre_mlp_layer_norm'''			)
		lowerCAmelCase							,       lowerCAmelCase												=  tax_mlp_lookup(_lowerCamelCase,					_lowerCamelCase,					'''encoder''',					_lowerCamelCase			)
		lowerCAmelCase												=  layer_norm
		if split_mlp_wi:
			lowerCAmelCase												=  wi[0].T
			lowerCAmelCase												=  wi[1].T
		else:
			lowerCAmelCase												=  wi.T
		lowerCAmelCase												=  wo.T

	lowerCAmelCase												=  old[
	    '''encoder/relpos_bias/rel_embedding'''
	].T
	lowerCAmelCase												=  old['''encoder/encoder_norm/scale''']

	if not is_encoder_only:
		# Decoder.
		for i in range(_lowerCamelCase			):
			# Block i, layer 0 (Self Attention).
			lowerCAmelCase												=  tax_layer_norm_lookup(_lowerCamelCase,					_lowerCamelCase,					'''decoder''',					'''pre_self_attention_layer_norm'''			)
			lowerCAmelCase							,       lowerCAmelCase							,       lowerCAmelCase							,       lowerCAmelCase												=  tax_attention_lookup(_lowerCamelCase,					_lowerCamelCase,					'''decoder''',					'''self_attention'''			)
			lowerCAmelCase												=  layer_norm
			lowerCAmelCase												=  k.T
			lowerCAmelCase												=  o.T
			lowerCAmelCase												=  q.T
			lowerCAmelCase												=  v.T

			# Block i, layer 1 (Cross Attention).
			lowerCAmelCase												=  tax_layer_norm_lookup(_lowerCamelCase,					_lowerCamelCase,					'''decoder''',					'''pre_cross_attention_layer_norm'''			)
			lowerCAmelCase							,       lowerCAmelCase							,       lowerCAmelCase							,       lowerCAmelCase												=  tax_attention_lookup(_lowerCamelCase,					_lowerCamelCase,					'''decoder''',					'''encoder_decoder_attention'''			)
			lowerCAmelCase												=  layer_norm
			lowerCAmelCase												=  k.T
			lowerCAmelCase												=  o.T
			lowerCAmelCase												=  q.T
			lowerCAmelCase												=  v.T

			# Block i, layer 2 (MLP).
			lowerCAmelCase												=  tax_layer_norm_lookup(_lowerCamelCase,					_lowerCamelCase,					'''decoder''',					'''pre_mlp_layer_norm'''			)
			lowerCAmelCase							,       lowerCAmelCase												=  tax_mlp_lookup(_lowerCamelCase,					_lowerCamelCase,					'''decoder''',					_lowerCamelCase			)
			lowerCAmelCase												=  layer_norm
			if split_mlp_wi:
				lowerCAmelCase												=  wi[0].T
				lowerCAmelCase												=  wi[1].T
			else:
				lowerCAmelCase												=  wi.T
			lowerCAmelCase												=  wo.T

		lowerCAmelCase												=  old['''decoder/decoder_norm/scale''']
		lowerCAmelCase												=  old[
		    '''decoder/relpos_bias/rel_embedding'''
		].T

		# LM Head (only in v1.1 checkpoints, in v1.0 embeddings are used instead)
		if "decoder/logits_dense/kernel" in old:
			lowerCAmelCase												=  old['''decoder/logits_dense/kernel'''].T

	return new




def __magic_name__	(				_lowerCamelCase:		Dict,					_lowerCamelCase:		bool			)					->						str:

	'''simple docstring'''




	lowerCAmelCase												=  collections.OrderedDict([(k, torch.from_numpy(v.copy()			)) for (k, v) in converted_params.items()]			)

	# Add what is missing.
	if "encoder.embed_tokens.weight" not in state_dict:
		lowerCAmelCase												=  state_dict['''shared.weight''']

	if not is_encoder_only:
		if "decoder.embed_tokens.weight" not in state_dict:
			lowerCAmelCase												=  state_dict['''shared.weight''']

		if "lm_head.weight" not in state_dict:  # For old 1.0 models.
			print('''Using shared word embeddings as lm_head.'''			)
			lowerCAmelCase												=  state_dict['''shared.weight''']

	return state_dict




def __magic_name__	(				_lowerCamelCase:		List[str],					_lowerCamelCase:		Union[str, Any],					_lowerCamelCase:		Optional[Any],					_lowerCamelCase:		List[Any]			)					->						List[str]:

	'''simple docstring'''




	lowerCAmelCase												=  checkpoints.load_tax_checkpoint(_lowerCamelCase			)
	lowerCAmelCase												=  convert_tax_to_pytorch(_lowerCamelCase,					num_layers=config.num_layers,					is_encoder_only=_lowerCamelCase			)
	lowerCAmelCase												=  make_state_dict(_lowerCamelCase,					_lowerCamelCase			)
	model.load_state_dict(_lowerCamelCase,					strict=_lowerCamelCase			)




def __magic_name__	(				_lowerCamelCase:		Optional[int],					_lowerCamelCase:		Optional[int],					_lowerCamelCase:		Union[str, Any],					_lowerCamelCase:		bool = False			)					->						Optional[Any]:

	'''simple docstring'''




	lowerCAmelCase												=  TaConfig.from_json_file(_lowerCamelCase			)
	print(F"""Building PyTorch model from configuration: {config}"""			)
	# Non-v1.1 checkpoints could also use T5Model, but this works for all.
	# The v1.0 checkpoints will simply have an LM head that is the word embeddings.
	if is_encoder_only:
		lowerCAmelCase												=  TaEncoderModel(_lowerCamelCase			)
	else:
		lowerCAmelCase												=  TaForConditionalGeneration(_lowerCamelCase			)

	# Load weights from tf checkpoint
	load_tax_weights_in_ta(_lowerCamelCase,					_lowerCamelCase,					_lowerCamelCase,					_lowerCamelCase			)

	# Save pytorch-model
	print(F"""Save PyTorch model to {pytorch_dump_path}"""			)
	model.save_pretrained(_lowerCamelCase			)

	# Verify that we can load the checkpoint.
	model.from_pretrained(_lowerCamelCase			)
	print('''Done'''			)


if __name__ == "__main__":
					UpperCAmelCase        =				argparse.ArgumentParser(description="""Converts a native T5X checkpoint into a PyTorch checkpoint.""")
					# Required parameters
					parser.add_argument(
					    """--t5x_checkpoint_path""", default=None, type=str, required=True, help="""Path to the T5X checkpoint."""
					)
					parser.add_argument(
					    """--config_file""",
					    default=None,
					    type=str,
					    required=True,
					    help="""The config json file corresponding to the pre-trained T5 model.\nThis specifies the model architecture.""",
					)
					parser.add_argument(
					    """--pytorch_dump_path""", default=None, type=str, required=True, help="""Path to the output PyTorch model."""
					)
					parser.add_argument(
					    """--is_encoder_only""", action="""store_true""", help="""Check if the model is encoder-decoder model""", default=False
					)
					UpperCAmelCase        =				parser.parse_args()
					convert_tax_checkpoint_to_pytorch(
					    args.tax_checkpoint_path, args.config_file, args.pytorch_dump_path, args.is_encoder_only
					)




"""simple docstring"""






from collections import namedtuple

UpperCAmelCase        =				namedtuple("""from_to""", """from_ to""")

UpperCAmelCase        =				{
    """cubicmeter""": from_to(1, 1),
    """litre""": from_to(0.001, 1_0_0_0),
    """kilolitre""": from_to(1, 1),
    """gallon""": from_to(0.00_454, 264.172),
    """cubicyard""": from_to(0.76_455, 1.30_795),
    """cubicfoot""": from_to(0.028, 35.3_147),
    """cup""": from_to(0.000_236_588, 4_226.75),
}




def __magic_name__	(				_lowerCamelCase:		float,					_lowerCamelCase:		str,					_lowerCamelCase:		str			)					->						float:

	'''simple docstring'''




	if from_type not in METRIC_CONVERSION:
		raise ValueError(
		    F"""Invalid 'from_type' value: {from_type!r}  Supported values are:\n"""
		    + ''', '''.join(_lowerCamelCase			)			)
	if to_type not in METRIC_CONVERSION:
		raise ValueError(
		    F"""Invalid 'to_type' value: {to_type!r}.  Supported values are:\n"""
		    + ''', '''.join(_lowerCamelCase			)			)
	return value * METRIC_CONVERSION[from_type].from_ * METRIC_CONVERSION[to_type].to


if __name__ == "__main__":
					import doctest

					doctest.testmod()


